SoroushVahidi/scidocs huggingface dataset:
Through the eyes of love: reality and illusion in intimate relationships.
(2010)

This article reviews the research literature and theory concerned with accuracy of judgments in romantic relationships. We initially propose a model of cognition in (romantic) relationships that distinguishes between 2 forms of accuracy: mean-level bias and tracking accuracy. We then report the results of meta-analyses of research on heterosexual, romantic relationships, which used external benchmarks and reported levels of tracking accuracy (98 studies) and/or mean-level bias (48 studies). The results revealed robust overall effect sizes for both tracking accuracy (r = .47) and positive mean-level bias (r = .09). As expected, the effects were substantial and positive for tracking accuracy across 6 judgmental categories, whereas signed mean-level bias was negative for the interaction attributions (e.g., love, communication). The results showed, as expected, that these 2 forms of accuracy were independent-the 2 kinds of effect size derived from the same set of 38 studies were uncorrelated. As expected, gender, relationship length, and relationship evaluations moderated mean-level bias across studies but (unexpectedly) not for tracking accuracy. In the Discussion we evaluate the prior model in light of the findings, other research, moderating variables (such as self-esteem), the role of projection, the early stages of mate selection, metacognition, and the rationality and nature of motivated cognition. We conclude that our model, findings, and analyses help to resolve the apparent paradox that love is both riven with illusions and rooted in reality, and support both evolutionary and social psychological approaches to understanding cognition in romantic relationships.
Aligning movies with scripts by exploiting temporal ordering constraints
(2016)

Scripts provide rich textual annotation of movies, including dialogs, character names, and other situational descriptions. Exploiting such rich annotations requires aligning the sentences in the scripts with the corresponding video frames. Previous work on aligning movies with scripts predominantly relies on time-aligned closed-captions or subtitles, which are not always available. In this paper, we focus on automatically aligning faces in movies with their corresponding character names in scripts without requiring closed-captions/subtitles. We utilize the intuition that faces in a movie generally appear in the same sequential order as their names are mentioned in the script. We first apply standard techniques for face detection and tracking, and cluster similar face tracks together. Next, we apply a generative Hidden Markov Model (HMM) and a discriminative Latent Conditional Random Field (LCRF) to align the clusters of face tracks with the corresponding character names. Our alignment models (especially LCRF) significantly outperform the previous state-of-the-art on two different movie datasets and for a wide range of face clustering algorithms.
Traffic sign detection and recognition for intelligent vehicle
(2011)

In this paper, we propose a computer vision based system for real-time robust traffic sign detection and recognition, especially developed for intelligent vehicle. In detection phase, a color-based segmentation method is used to scan the scene in order to quickly establish regions of interest (ROI). Sign candidates within ROIs are detected by a set of Haar wavelet features obtained from AdaBoost training. Then, the Speeded Up Robust Features (SURF) is applied for the sign recognition. SURF finds local invariant features in a candidate sign and matches these features to the features of template images that exist in data set. The recognition is performed by finding out the template image that gives the maximum number of matches. We have evaluated the proposed system on our intelligent vehicle SmartVII. A recognition accuracy of over 90% in real-time has been achieved.
An effective approach for active tracking with a PTZ camera
(2010)

The concept of active tracking is presented to simulate the characteristics of human vision in intelligent visual surveillance. The Pan/Tilt/Zoom (PTZ) camera is generally used for active tracking. In this paper, we present a novel and effective approach for active object tracking with a PTZ camera, and construct a near real-time system for indoor and outdoor scenes. The tracking algorithm of our system is based on the feature matching, with the PID control to drive the camera. The feature extracted from moving people is described as a region covariance matrix which combines the spatial and statistical properties of the targets (e.g. coordinates, color, and gradient). Results from indoor and outdoor experiments demonstrate the effectiveness and accuracy of our approach.
Autonomous vehicles : Pedestrian heaven or pedestrian hell ?
(2017)

Autonomous vehicles are an emerging technology that will play a significant role in shaping transport systems in the decades to come. Consequently, implications are anticipated for societal issues ranging from transport safety to congestion, from energy consumption to the allocation of public and private lands. Driverless technology will undoubtedly influence other transport modes, too. With this in mind, the implications for pedestrians have hardly been researched. If the introduction of driverless cars could ultimately result in the disappearance of on-street parking spaces and in crossing streets without so much as glancing towards approaching cars, this could potentially mean that walking becomes much more attractive. At the same time, one could imagine the opposite outcome if society decides that the ability of being picked up and dropped off at any front door or location imaginable renders walking as a state-supported transport mode superfluous. The purpose of this paper is to discuss the impact of autonomous vehicles on pedestrian transport in all its aspects, including the range of possible outcomes, interdependencies, and opposing effects. With the state-of-the-art of pedestrian transport research as a starting point, how can the needs of pedestrians be taken into consideration when fully autonomous vehicles become ubiquitous in daily life?
Controller design and fault tolerance analysis of 4-phase floating interleaved boost converter for fuel cell electric vehicles
(2017)

Owing to the energy shortage and the increasingly serious environmental pollution, fuel cell electric vehicles (FCEV) with zero-emission and high-efficiency have been expected to be the most potential candidate to substitute the conventional vehicles. The DC/DC converter is the interface between the fuel cell (FC) and the driveline of FCEV. It not only needs high voltage gain to convert the wide FC voltage into an appropriate voltage level, but also needs the capacity of fault tolerance to enhance the reliability of the system. For this reason, floating interleaved boost converters (FIBC) seem to be the optimal selection. Despite this topology can continue operating without interruption under the action of the proper control scheme in the case of power switch open circuit fault (OCF), operating in degraded mode has adverse impacts on the component stress and the input current ripple. Hence, this paper aims to design an effective controller to maintain the dc bus voltage constant and to demonstrate thorough theoretical analysis and simulation verification of these undesirable effects.
Cross-Genre Author Profile Prediction Using Stylometry-Based Approach
(2016)

Author profiling task aims to identify different traits of an author by analyzing his/her written text. This study presents a Stylometry-based approach for detection of author traits (gender and age) for cross-genre author profiles. In our proposed approach, we used different types of stylistic features including 7 lexical features, 16 syntactic features, 26 character-based features and 6 vocabulary richness (total 56 stylistic features). On the training corpus, the proposed approach obtained promising results with an accuracy of 0.787 for gender, 0.983 for age and 0.780 for both (jointly detecting age and gender). On the test corpus, proposed system gave an accuracy of 0.576 for gender, 0.371 for age and 0.256 for both.
Optical interconnects for extreme scale computing systems
(2017)

Large-scale high performance computing is permeating nearly every corner of modern applications spanning from scientific research and business operations, to medical diagnostics, and national security. All these communities rely on computer systems to process vast volumes of data quickly and efficiently, yet progress toward increased computing power has experienced a slowdown in the last number of years. The sheer cost and scale, stemming from the need for extreme parallelism, are among the reasons behind this stall. In particular, very large-scale, ultra-high bandwidth interconnects, essential for maintaining computation performance, represent an increasing portion of the total cost budget. Photonic systems are often cited as ways to break through the energy-bandwidth limitations of conventional electrical wires toward drastically improving interconnect performance. This paper presents an overview of the challenges associated with large-scale interconnects, and reviews how photonic technologies can contribute to addressing these challenges. We review some important aspects of photonics that should not be underestimated in order to truly reap the benefits of cost and power reduction. © 2017 Elsevier B.V. All rights reserved.
Asynchronous Multi-task Learning
(2016)

Many real-world machine learning applications involveseveral learning tasks which are inter-related. For example, in healthcare domain, we need to learn a predictive model of a certain disease for many hospitals. The models for each hospital may be different because of the inherent differences in the distributions of the patient populations. However, the models are also closely related because of the nature of the learning tasks modeling the same disease. By simultaneously learning all the tasks, multi-task learning (MTL) paradigm performs inductive knowledge transfer among tasks to improve the generalization performance. When datasets for the learning tasks are stored at different locations, it may not always be feasible to transfer the data to provide a data centralized computing environment due to various practical issues such as high data volume and privacy. In this paper, we propose a principled MTL framework for distributed and asynchronous optimization to address the aforementioned challenges. In our framework, gradient update does not wait for collecting the gradient information from all the tasks. Therefore, the proposed method is very efficient when the communication delay is too high for some task nodes. We show that many regularized MTL formulations can benefit from this framework, including the low-rank MTL for sharedsubspace learning. Empirical studies on both synthetic and realworld datasets demonstrate the efficiency and effectiveness of the proposed framework.
A Survey of Evaluation Methods and Measures for Interpretable Machine Learning
(2018)

The need for interpretable and accountable intelligent system gets sensible as artificial intelligence plays more role in human life. Explainable artificial intelligence systems can be a solution by self-explaining the reasoning behind the decisions and predictions of the intelligent system. Researchers from different disciplines work together to define, design and evaluate interpretable intelligent systems for the user. Our work supports the different evaluation goals in interpretable machine learning research by a thorough review of evaluation methodologies used in machine-explanation research across the fields of human-computer interaction, visual analytics, and machine learning. We present a 2D categorization of interpretable machine learning evaluation methods and show a mapping between user groups and evaluation measures. Further, we address the essential factors and steps for a right evaluation plan by proposing a nested model for design and evaluation of explainable artificial intelligence systems.
BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA
(2016)

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.
The economic costs of pain in the United States.
(2012)

UNLABELLED
In 2008, according to the Medical Expenditure Panel Survey (MEPS), about 100 million adults in the United States were affected by chronic pain, including joint pain or arthritis. Pain is costly to the nation because it requires medical treatment and complicates treatment for other ailments. Also, pain lowers worker productivity. Using the 2008 MEPS, we estimated 1) the portion of total U.S. health care costs attributable to pain; and 2) the annual costs of pain associated with lower worker productivity. We found that the total costs ranged from $560 to $635 billion in 2010 dollars. The additional health care costs due to pain ranged from $261 to $300 billion. This represents an increase in annual per person health care costs ranging from $261 to $300 compared to a base of about $4,250 for persons without pain. The value of lost productivity due to pain ranged from $299 to $335 billion. We found that the annual cost of pain was greater than the annual costs of heart disease ($309 billion), cancer ($243 billion), and diabetes ($188 billion). Our estimates are conservative because they do not include costs associated with pain for nursing home residents, children, military personnel, and persons who are incarcerated.


PERSPECTIVE
This study estimates that the national cost of pain ranges from $560 to $635 billion, larger than the cost of the nation's priority health conditions. Because of its economic toll on society, the nation should invest in research, education, and training to advocate the successful treatment, management, and prevention of pain.
The characteristics of persistent sexual offenders: a meta-analysis of recidivism studies.
(2005)

A meta-analysis of 82 recidivism studies (1,620 findings from 29,450 sexual offenders) identified deviant sexual preferences and antisocial orientation as the major predictors of sexual recidivism for both adult and adolescent sexual offenders. Antisocial orientation was the major predictor of violent recidivism and general (any) recidivism. The review also identified some dynamic risk factors that have the potential of being useful treatment targets (e.g., sexual preoccupations, general self-regulation problems). Many of the variables commonly addressed in sex offender treatment programs (e.g., psychological distress, denial of sex crime, victim empathy, stated motivation for treatment) had little or no relationship with sexual or violent recidivism.
A Novel Compact UHF RFID Tag Antenna Designed With Series Connected Open Complementary Split Ring Resonator (OCSRR) Particles
(2010)

A novel compact planar antenna for passive UHF radio frequency identification (RFID) tags is presented. Instead of using meander-line sections, much smaller open complementary split ring resonator (OCSRR) particles are connected in series to create a small dipole with a conjugate match to the power harvesting circuit on the passive RFID tag. The manufactured (prototype) OCSRR RFID tag presented here has an antenna input impedance of 15.8 + J142.5 Ω at a frequency of 920 MHz and a max read range of 5.48 m. This performance is achieved with overall tag dimensions of 0.036λ0 × 0.17λ0 where λ0 is the free space wavelength of 920 MHz.
Path Planning Based on Bézier Curve for Autonomous Ground Vehicles
(2008)

In this paper we present two path planning algorithms based on Bezier curves for autonomous vehicles with way points and corridor constraints. Bezier curves have useful properties for the path generation problem. The paper describes how the algorithms apply these properties to generate the reference trajectory for vehicles to satisfy the path constraints. Both algorithms join cubic Bezier curve segments smoothly to generate the path. Additionally, we discuss the constrained optimization problem that optimizes the resulting path for a user-defined cost function. The simulation shows the generation of successful routes for autonomous vehicles using these algorithms as well as control results for a simple kinematic vehicle. Extensions of these algorithms towards navigating through an unstructured environment with limited sensor range are discussed.
A multi-DoF anthropomorphic transradial prosthetic arm
(2014)

An anthropomorphic transradial prosthetic arm is proposed in this paper. In order to generate the wrist flexion/extension and ulna/radial deviation, a novel wrist mechanism is proposed based on the parallel prismatic manipulators. It is expected to realize high speed operation, higher positional accuracy and anthropomorphic features using the proposed mechanism. The prosthetic arm consists of an under-actuated hand as the terminal device. The hand mechanism is capable of providing the grasping adaptation. With the intention of verifying the effectiveness of the mechanisms in motion generation, motion simulation and kinematic analysis are carried out.
SemEval-2007 Task 02: Evaluating Word Sense Induction and Discrimination Systems
(2007)

Word Sense Disambiguation (WSD) is a key enabling-technology. Supervised WSD techniques are the best performing in public evaluations, but need large amounts of hand-tagging data. Existing hand-annotated corpora like SemCor (Miller et al., 1993), which is annotated with WordNet senses (Fellbaum, 1998) allow for a small improvement over the simple most frequent sense heuristic, as attested in the all-words track of the last Senseval competition (Snyder and Palmer, 2004). In theory, larger amounts of training data (SemCor has approx. 500M words) would improve the performance of supervised WSD, but no current project exists to provide such an expensive resource. Another problem of the supervised approach is that the inventory and distribution of senses changes dramatically from one domain to the other, requiring additional hand-tagging of corpora (Martı́nez and Agirre, 2000; Koeling et al., 2005). Supervised WSD is based on the “fixed-list of senses” paradigm, where the senses for a target word are a closed list coming from a dictionary or lexicon. Lexicographers and semanticists have long warned about the problems of such an approach, where senses are listed separately as discrete entities, and have argued in favor of more complex representations, where, for instance, senses are dense regions in a continuum (Cruse, 2000). Unsupervised Word Sense Induction and Discrimination (WSID, also known as corpus-based unsupervised systems) has followed this line of thinking, and tries to induce word senses directly from the corpus. Typical WSID systems involve clustering techniques, which group together similar examples. Given a set of induced clusters (which represent word uses or senses1), each new occurrence of the target word will be compared to the clusters and the most similar cluster will be selected as its sense. One of the problems of unsupervised systems is that of managing to do a fair evaluation. Most of current unsupervised systems are evaluated in-house, with a brief comparison to a re-implementation of a former system, leading to a proliferation of unsupervised systems with little ground to compare among them. The goal of this task is to allow for comparison across sense-induction and discrimination systems, and also to compare these systems to other supervised and knowledge-based systems. The paper is organizes as follows. Section 2 presents the evaluation framework used in this task. Section 3 presents the systems that participated in the task, and the official results. Finally, Section 4 draws the conclusions.
Fossils, genes and the evolution of animal limbs
(1997)

The morphological and functional evolution of appendages has played a crucial role in the adaptive radiation of tetrapods, arthropods and winged insects. The origin and diversification of fins, wings and other structures, long a focus of palaeontology, can now be approached through developmental genetics. Modifications of appendage number and architecture in each phylum are correlated with regulatory changes in specific patterning genes. Although their respective evolutionary histories are unique, vertebrate, insect and other animal appendages are organized by a similar genetic regulatory system that may have been established in a common ancestor.
The DL-Lite Family and Relations
(2009)

The recently introduced series of description logics under the common moniker ‘DLLite’ has attracted attention of the description logic and semantic web communities due to the low computational complexity of inference, on the one hand, and the ability to represent conceptual modeling formalisms, on the other. The main aim of this article is to carry out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes: by (i) adding the Boolean connectives and (ii) number restrictions to concept constructs, (iii) allowing role hierarchies, (iv) allowing role disjointness, symmetry, asymmetry, reflexivity, irreflexivity and transitivity constraints, and (v) adopting or dropping the unique name assumption. We analyze the combined complexity of satisfiability for the resulting logics, as well as the data complexity of instance checking and answering positive existential queries. Our approach is based on embedding DL-Lite logics in suitable fragments of the one-variable first-order logic, which provides useful insights into their properties and, in particular, computational behavior.